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Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology

Author

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  • Özden Tozanlı
  • Elif Kongar
  • Surendra M. Gupta

Abstract

Growing environmental awareness and widening extended producer responsibility have heightened the need for economically, environmentally, and socially sustainable business strategies levered by digital technologies. As an extension, various take-back policies focusing on product waste and recovery are put in place by the high-tech manufacturing industry. With an attempt to increase sales while ensuring the environmental sustainability of products, trade-in programmes that incentivize consumers to exchange used goods for new and most recent technology products became a value-adding strategy for businesses. Due to the high unpredictability in the quality of returned devices however, determining trade-in margins is a challenging task for original equipment manufacturers (OEMs). This inevitably reveals the need for incorporating intelligent technologies into the formation of manufacturing and logistics architectures to simultaneously preserve OEMs profitability and ensure the sustainable development of the closed-loop supply chain activities. With this motivation, this study presents the use of IoT-embedded products in a blockchain-enabled disassembly-to-order system to determine the optimal trade-in-to-upgrade policy. A discrete-event simulation model is developed to obtain the expected cost of the disassembly-to-order system. Optimal incentives for varying product qualities are then computed by utilising this cost in the trade-in policy model.

Suggested Citation

  • Özden Tozanlı & Elif Kongar & Surendra M. Gupta, 2020. "Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7183-7200, December.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:23:p:7183-7200
    DOI: 10.1080/00207543.2020.1712489
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